DeepSpeed VS server

Compare DeepSpeed vs server and see what are their differences.


DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. (by microsoft)


The Triton Inference Server provides an optimized cloud and edge inferencing solution. (by triton-inference-server)
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DeepSpeed server
41 19
25,088 5,370
61.0% 6.0%
9.6 8.3
2 days ago 3 days ago
Python Python
Apache License 2.0 BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of DeepSpeed. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-11.


Posts with mentions or reviews of server. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-21.
  • Machine Learning Inference Server in Rust?
    4 projects | | 21 Mar 2023
    I am looking for something like [Triton Inference Server]( or [TFX Serving](, but in Rust. I came across [Orkon]( which seems to be dormant and a bunch of examples off of the [Awesome-Rust-MachineLearning](
  • Multi-model serving options
    3 projects | | 12 Feb 2023
    You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.
  • I mean,.. we COULD just make our own lol
    4 projects | | 12 Feb 2023
  • Why TensorFlow for Python is dying a slow death
    4 projects | | 15 Jan 2023
    "TensorFlow has the better deployment infrastructure"

    Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both ways. It's so tightly coupled to Tensorflow if the mlops side of the solution is using Tensorflow Serving you're going to get "trapped" in the Tensorflow ecosystem (essentially).

    For pytorch models (and just about anything else) I've been really enjoying Nvidia Triton Server[0]. Of course it further entrenches Nvidia and CUDA in the space (although you can execute models CPU only) but for a deployment today and the foreseeable future you're almost certainly going to be using a CUDA stack anyway.

    Triton Server is very impressive and I'm always surprised to see how relatively niche it is.

    [0] -

  • Show HN: Software for Remote GPU-over-IP
    6 projects | | 14 Dec 2022
    Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.

    Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.

    So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.

    Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.

    Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.

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  • Exploring Ghostwriter, a GitHub Copilot alternative
    3 projects | | 8 Nov 2022
    Replit built Ghostwriter on the open source scene based on Salesforce’s Codegen, using Nvidia’s FasterTransformer and Triton server for highly optimized decoders, and the knowledge distillation process of the CodeGen model from two billion parameters to a faster model of one billion parameters.
  • [D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
    8 projects | | 28 Oct 2022
    For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.
  • Popular Machine Learning Deployment Tools
    4 projects | | 16 Apr 2022
  • Nvidia Fiscal Q3 2022 Financial Result
    4 projects | | 17 Nov 2021
    Tools for developing and deploying large language models: NVIDIA NeMo Megatron, for training models with trillions of parameters; the Megatron 530B customizable LLM that can be trained for new domains and languages; and NVIDIA Triton Inference Server™ with multi-GPU, multinode distributed inference functionality.
  • Triton: Open-Source GPU Programming for Neural Networks
    3 projects | | 28 Jul 2021
    Unfortunate name clash with NVIDIAs Triton Inference Server:

What are some alternatives?

When comparing DeepSpeed and server you can also consider the following projects:

ColossalAI - Making large AI models cheaper, faster and more accessible

fairscale - PyTorch extensions for high performance and large scale training.

TensorRT - NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.

Megatron-LM - Ongoing research training transformer models at scale

fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

mesh-transformer-jax - Model parallel transformers in JAX and Haiku

llama - Inference code for LLaMA models

gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

text-generation-webui - A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.

Finetune_LLMs - Repo for fine-tuning GPTJ and other GPT models

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX